Statistical correction of daily precipitation sums from climate models

Climate change prediction and evaluation of its impact currently represent one of the key challenges for the science community. Regional climate models (RCM) have been recently established as a main source of the data for climate change assessment studies. Nevertheless, RCM outputs suffer from systematic errors caused primarily by their low spatial resolution and cannot be used directly without any form of bias correction. The bias correction is an actual topic in climatology and several correction methods were developed, ranging from the simple additive method to more advanced approaches (e.g. quantile mapping). However, despite this progress, the bias correction methods suffer from several difficulties, which bring another source of uncertainty into the climate change impact assessment studies. This thesis is focused on two problematic points connected with the bias correction of daily precipitation data. The first one is a non-stationarity between calibration and application periods. New correction methods are developed, showing an increased resistance to non-stationary conditions. The second problem is related to the correction of a dependence (i.e. correlation and covariance) structure of multivariate precipitation data. A new procedure is proposed, correcting the complete dependence structure of the model data. All newly introduced methods are validated using measured and RCM-simulated data; the validation demonstrates their suitable applicability